Targeted marketing to on-hold customer

ABSTRACT

Systems and methods are described for delivering targeted content to a customer through a voice response unit (VRU). When the customer calls into the VRU, the customer is prompted to enter identification information. A unique customer identification code (UCIC) associated with the customer is used to look up an anonymized customer profile stored by a third party server. An advertisement identification code (ADIC) associated with the anonymized customer profile is used to identify targeted offers for the customer. The customer is then informed of the targeted offers verbally by the VRU. Optionally, the customer is presented with details of the offer verbally via the VRU. According to another aspect, the customer can select alternate media for delivery of the offer details (e.g., email, text message, etc.).

This application claims priority to U.S. Provisional Application Ser.No. 61/332,937, filed on May 10, 2010. This application is also acontinuation-in-part application of U.S. patent application Ser. No.12/403,656, filed Mar. 13, 2009 (METHOD AND SYSTEM FOR TARGETED CONTENTPLACEMENT), which in turn is a continuation-in-part of U.S. patentapplication Ser. No. 12/266,199, filed Nov. 6, 2008 (METHOD AND SYSTEMFOR TARGETED CONTENT PLACEMENT), which claims priority to U.S. PatentApplication 61/037,020 filed Mar. 17, 2008 (METHOD AND SYSTEM FORTARGETED INTERNET ADVERTISEMENT PLACEMENT) which are incorporated hereinby reference in their entireties. U.S. patent application Ser. No.11/865,466 filed Oct. 1, 2007 (PERSONALIZED CONSUMER ADVERTISINGPLACEMENT) is also incorporated by reference herein.

BACKGROUND

1. Field

The following description relates generally to the targeting of mediacontent to consumers. More particularly, the embodiments relate to dataarchitecture and processing, and a database solution that willultimately deliver personalized content, including advertisements,video, audio, audio/visual, text-based content, or any personalizedmedia to a device, where the device can store a delivered identificationand is operated by a consumer who is classified in a classificationderived from financial information for that consumer.

2. Description of Related Art

Increasing Internet usage where nearly one billion global users accessthe Internet on at least a monthly basis is reflected by greatercorporate spending on Internet advertising and more compelling andeffective marketing technologies and practices. For example, Standardand Poor states that keyword search revenues contributed notably tothese gains, with 182% growth in 2003, 51% growth in 2004, and 34%growth in 2005, largely reflecting the successes of Google and Yahoo.These companies garner revenues from specific user on-line queries thatgenerate sponsored Internet links and associated click-throughs. Thispattern of growth in Internet advertising is anticipated to continuewell into the future.

To make their advertising dollars more effective, advertisers attempt totarget their advertising to individuals who are more likely to have aninterest in the advertised product, thereby producing a higherclick-through rate and increased revenues. Of course, in order to targetindividuals with any degree of accuracy, something must be known aboutthe individual. For this reason, technologies have been developed forwhat is known in the art as behavioral targeting based on tracking auser's habits through monitoring of the websites that the user visits,and offering targeted advertising based on the content of the visitedwebsites. It is assumed, for example, that if a user is visitingautomobile oriented websites, then an automobile oriented advertisementis more likely to generate a user response than one for breakfastcereal. A problem with this type of website tracking is that if anautomobile advertisement for a very expensive car is delivered to a userand he cannot afford to purchase the automobile, then this advertisementis not very effective.

While the above-described behavioral profiling has been somewhateffective in improving the effectiveness of Internet advertising, suchbehavioral profiling is unable to accumulate data related to anindividual's particular spending habits. For this reason, some marketershave developed methods to retain customers who have initiated purchasesfrom them by tracking their purchasing habits and trends. However, thistracking of purchases is limited to knowledge of purchases placed on themarketer's own websites. While a marketer such as, e.g., Amazon mighttrack a consumer's shopping habits so that when the consumer logs intoan Amazon website at a future time, advertisements can be automaticallyplaced showing suggested items for the consumer to consider based ontheir previous purchasing habits. Of course, Amazon would have noknowledge of the customer's purchasing habits at retailers notaffiliated with Amazon. Therefore, the tracking methodologies used byindividual on-line retailers are limited in the benefit that they canprovide to the retailer.

Furthermore, when a customer calls in to a business (e.g., a bank or thelike) and is placed on hold, advertisements may be presented to thecustomer during the on-hold period. However, such advertisements are nottargeted to the specific customer, and therefore are inefficient.

Accordingly, there is an unmet need for systems and/or methods thatfacilitate presenting targeted advertisements to a customer over thephone while the customer is on hold, and the like, while overcoming theaforementioned deficiencies.

SUMMARY

A method for presenting targeted content to a customer of a financialinstitution through a voice response unit (VRU) comprises receiving acall from a customer at a VRU, identifying the customer, and retrievingan anonymized customer profile for the customer. The method furthercomprises matching one or more targeted offers to the customer as afunction of key lifestyle indicators (KLIs) in the anonymized customerprofile. The VRU verbally informs the customer that the one or moretargeted offers is available to the customer. The offers can bedelivered to the customer while the customer is on hold or via a mediumof the customer's choosing (e.g., email, text message, social media,voicemail, etc.

A system for presenting targeted content to a customer of a financialinstitution through a voice response unit (VRU) comprises a voiceresponse system (VRU) that receives a call from the customer, andreceives customer identification information by which the customer isidentified. The system further includes a third-party server comprisinga processor and a memory. The processor executes computer-executableinstructions for retrieving an anonymized customer profile for thecustomer, and for executing a matching algorithm that identifies one ormore targeted offers for the customer as a function of key lifestyleindicators (KLIs) in the anonymized customer profile. The VRU informsthe customer that the one or more identified targeted offers isavailable to the customer.

The foregoing summary is provided only by way of introduction. Allfeatures, benefits, and advantages of the personalized consumer contentarchitecture/solution may be realized and obtained by instrumentalitiesand combinations particularly pointed out in the claims. Nothing in thissection should be taken as a limitation on the claims, which define thescope of the invention.

The subject development is also applicable to the other entities orfinancial institutions who maintain personalized web sites inassociation with customers' financial data, such as insurers, investmentcounselors, brokers or the like.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram depicting processes for generating lifestyleindicators for consumers.

FIG. 2 is a block diagram depicting processes for matching receipt-levelretail transactions to account-level bank transactions.

FIG. 3 is a block diagram depicting processes for creating and updatingan advertising campaign based on the classifications.

FIG. 4 is a flow diagram depicting a method for generating lifestyleindicators for consumers.

FIG. 5 is a flow diagram depicting a method for matching receipt-levelretail transactions to account-level bank transactions.

FIG. 6 is a flow diagram depicting a method for creating and updating anadvertising campaign based on the classifications.

FIG. 7 is a block diagram depicting a system for generatingclassifications for consumers, and for creating and updating anadvertising campaign based on the classifications.

FIG. 8 is a block diagram depicting a method for generating apersonalized advertisement.

FIG. 9 is a flow diagram depicting a method for delivering targetedcontent to a networked device.

FIG. 10 is a flow diagram depicting a method for recording a hashed IPaddress.

FIG. 11 is a flow diagram depicting another method for deliveringtargeted content to a networked device.

FIG. 12 is a flow diagram depicting a method for processing a list ofcustomers to receive targeted content.

FIG. 13 illustrates a system that facilitates notifying a customer of atargeted offer or reward to a customer when the customer calls in to thebusiness and is placed on hold and/or navigates an automated menupresented by a voice response unit (VRU).

FIG. 14 illustrates a system that facilitates presenting targetedcontent directly to a customer when the customer calls in to thebusiness and is placed on hold and/or navigates a menu presented by aVRU, wherein the targeted content (e.g., offer details) is provided tothe VRU for direct, audible presentation to the customer.

FIG. 15 illustrates a method for notifying a customer, while thecustomer is interacting with a VRU, that targeted content is availablefor the customer.

FIG. 16 illustrates a method for notifying a customer, while thecustomer is interacting with a VRU, that targeted content is availablefor the customer.

DESCRIPTION OF THE EMBODIMENTS

The following is a description of a system and method of deliveringpersonalized content, e.g. advertisements, video, audio, audio/visualand text-based content, for an anonymous network user on a network suchas, e.g., the Internet. An example of such a system is described so thatone may construct the system, however, the embodiments which are definedby the appended claims are not limited to the system described herein.

With reference to FIG. 1, a diagram is provided in block form forshowing the flow of information in a system for determiningclassifications for individuals who will be receiving targeted contentover a network, e.g., the Internet. The diagram is provided for thepurpose of explaining interrelationships between various data in thesystem, and the embodiments of the present application are not limitedto the arrangement shown. Although each of the blocks in the diagram isdescribed sequentially in a logical order, it is not to be assumed thatthe system processes the described information in any particular orderor arrangement.

Before describing each of the processes shown in the figure in detail, abrief explanation of terms and object of the processing is provided.Four types of information are described in FIG. 1. Bank consumerinformation 14 is anonymous demographic data about a consumer as anindividual such as, e.g. birth date, gender, zip code, etc. The bankconsumer information utilized in preferred embodiments is data that doesnot reveal the identity of the consumer. Accordingly, the bank consumerinformation used in the embodiment described herein does not includename, street address, social security number (or any other governmentissued identification number), e-mail address or telephone number. Bankaccount type information 13, on the other hand, while also beingassociated with the consumer, provides information related to an accountor accounts held by the consumer at the bank or financial institution.The account type information includes data describing the type ofaccount(s), e.g., savings, checking, mortgage, IRA, credit card account,asset loan etc. It also includes information related to each account, asapplicable, such as inception date, terms for mortgages or certificatesof deposit, payment amounts for mortgages or other loans, and so forth.Similar to the bank consumer information, in the embodiment describedherein the bank account type information does not include the accountnumber of the consumer. This further protects the privacy of theconsumer by anonymously identifying the consumer in a manner that willbe described in more detail herein. Bank transaction data 10 describesindividual transactions transacted by the consumer such as, e.g. acredit/debit card purchase at a retailer location. The bank transactiondata includes information such as a merchant/retailer identifier, thetotal transaction dollar amount of the transaction, the type oftransaction, the location of the merchant, etc. Bank transaction dataalso includes automated clearing house (ACH) transactions and automaticwithdrawals. The bank transaction data 10 typically does not, however,provide detailed line item information for each item that was part of amultiple-item purchase. In preferred embodiments, however, retailers orservice providers opt to provide receipt-level retail transactioninformation 32. This information is more detailed and includes line itemdetail identifying each item and quantity purchased from theretailer/service provider, along with the price of the individual items.Although not essential to determining classifications, described morefully below, the system and processes described herein can protect theidentity of the consumer by anonymously identifying the consumer basedon the data described above. This can protect the consumer from securitybreaches, where the consumer's identity can be used to his detriment,e.g. using a consumer's identity for fraudulent transactions.

The use of each of the above-described types of information is describedin more detail below. However, an object of embodiments of the presentapplication is to classify consumers into lifestyles or classificationsbased on the above-described information. These classified lifestylesare provided so that targeted content can be delivered to the consumersbased on the lifestyle of the particular consumer. The classificationscan also protect a consumer's identity by referring to a consumer as amember of a class as opposed to as a specific individual. Theclassifications can be deduced based on the status of the consumer,e.g., whether the consumer is a grandparent, a college student, a singlemother, etc. The classification can be based on the activities in whichthe consumer participates such as golfing or attending theatres. Theclassification can also be based on what the consumer owns, e.g., a homeowner or a boat owner, or on the income of the consumer. Theclassification can include multiple classifications, e.g. a grandparentwho golfs. The classification is meant to classify consumers intocategories helpful to merchandisers seeking to deliver targetedadvertisements. The classifications can also help publishers to providemore meaningful content to viewers of the content. Examples of suchclassifications include, but are not limited to grandparent, collegestudent, self employed, theatre/movie attendee, investor, parent,traveler, region of residence or travel, etc. The classificationsdescribed herein are hierarchical in nature. For example, if a consumeris identified and classified with a classification as a “mother”, theclassification for mother falls under a higher-level classification for“female”. This hierarchical relationship is discussed in further detailherein.

First described, are incoming bank transactions. Although the term “banktransaction” is used throughout this application, it is to beappreciated that the transaction may be related to any form of financialinstitution with which a user of the system conducts business or has arelationship. Typically, the financial institution will provide on-lineservices for the user enabling the user by means of a unique user ID andpassword, or other security data, to either log in or sign in with awebsite or other networked portal or application affiliated thefinancial institution for conducting business. When the user logs on tothe financial institution's website or other networked application, theuser is identified by a unique customer identification code (UCIC)assigned by the financial institution. The UCIC does not include thefinancial institution's customer password or unique user ID used to logonto the financial institution's website, nor does it include thecustomer's account number, name, street address, social security number,e-mail address or telephone number. The UCIC is matched to an anonymousidentifier, hereinafter referred to as an advertisement deliveryidentification code (ADIC), which anonymously but uniquely identifiesthe user. Even though the term ADIC is used to refer to theidentification, this identification can be useful when delivering othertargeted content, e.g. video, text-based content that does not market aproduct. The ADIC is assigned by the entity that will deliver thetargeted content or will associate with another publisher to delivermore relevant content. Both the ADIC and the UCIC are described in moredetail in the above-referenced application Ser. No. 11/865,466. The ADICcan also be specific to the device the financial institution's customeruses to access the financial institution's website or other networkedapplication. For example, if the one customer use a lap top computer anda mobile phone to access the financial institution's website or othernetworked application, this same customer can be associated with twodifferent ADICs, one for each device.

Three types of information received from financial institutions arediscussed herein, i.e., bank consumer information, account typeinformation, and account-level bank transaction information. Aspreviously described, each received bank transaction includesinformation relating to a particular bank transaction, and the bankconsumer information includes account-level detail informationpertaining to the particular consumer. For example, the system receivesconsumer level information 14 that may include birthday and zip codewhich are useful so that, e.g., age and location can be determined forthe consumer. Account type information 13 provides additionalinformation related to the consumer such as the types of accounts heldat the financial institution. The account-level bank transaction 10, onthe other hand, provides information related to a purchase of goods orservices from a merchant. It is to be understood, however, in preferredembodiments, the received information does not reveal the identity ofthe consumer, e.g., name, street address, social security number,telephone number, account number, credit card number, e-mail address,etc.

The account-level bank transaction data 16 includes, e.g., merchant SICcode, merchant description, price, asset information if the transactionrelates to a loan, location of the merchant, preferably a zip code forthe merchant, and the type of transaction. The standard industrialclassification (SIC) is a code developed to classify establishments bythe type of activity in which they are primarily engaged. For example,the SIC code 2043 represents cereal breakfast foods, and SIC code 4521represents department stores. It is to be appreciated that theaccount-level bank transaction data 16 described herein is exemplaryonly. The data contained in the account-level bank transaction recordcan vary according to both embodiment and according to the financialinstitution providing the bank transaction 10. For example, the SIC codeis being replaced with the North Atlantic Industry Classification System(NAICS). The NAICS code serves substantially the same purpose as the SICcode, however, the NAICS code has been extended to six digits incontrast to the four digit SIC code in order to accommodate a largernumber of sectors and allow more flexibility in designating subsectors.The merchant description is usually a string of characters, which can beutilized in on-line banking, describing the particular merchant. Theasset code, utilized with loans, can be useful for providing additionalinformation. For example, each time a car payment is made, the systemcan determine what kind of car the consumer drives based on theinformation provided, e.g., the asset for which the loan was made.

The information provided in the account-level bank transaction data 16and the bank consumer information data 14 can be useful in classifyingcustomers and in developing the classifications 26. For example, thebank consumer information 14 provides a zip code related to theconsumer, and the account-level bank transaction data 16 can provide azip code related to the merchant. Based on these two zip codes, onanalyzing purchases made for gasoline, the system is able to deduce whatthe consumer's approximate daily commute is, and how much the consumeris spending on gasoline. With this information, the consumer can beclassified into a classification “commuter between 15-25 miles.”Similarly, other classifications can be deduced from the transactiondata. Another example of a classification is the user's debt/equityratio, which represents the amount of debt for the user versus theamount of cash savings at the bank, or in the case of assets, someassets have an appraised value such as a car, home, etc. The debt/equityratio can be deduced based on bank transaction data 16 and can be usefulin targeting advertising to a firm or advertiser seeking investors.

As the bank consumer information 14 and the account-level banktransaction data 16 are being received, the information data is funneledthrough a taxonomy classification system 18. The taxonomy classificationsystem 18 is where the system starts developing classification data. Forexample, based on analysis of the consumer information and account-leveltransaction information, the system can determine if the consumer is anSUV driver (based on the asset for which a loan has been secured), ahomeowner (based on mortgage information), etc. The taxonomy systemclassifies consumers according to a classification system similar to thepreviously described SIC codes and NAICS codes or any industry supportedclassification. The SIC and NAICS codes however, are typicallyinadequate for purposes of embodiments of the present application and,therefore, a taxonomy classification coding that can go much deeper andprovide more detailed classifications is utilized. Some suitabletaxonomy classifications are known in the art, and it is to beappreciated that any arbitrary taxonomy classification coding or systemcan be utilized by the taxonomy classification system 18. Developing ataxonomy classification system, however, is an intensive andtime-consuming process. Therefore, embodiments of the presentapplication may utilize existing taxonomy classifications when possible.

An example here is useful in describing how the taxonomy classificationsystem is able to deduce classifications based on the incoming bankconsumer information and account-level bank transaction data. Forexample, an account-level bank transaction is received including an SICcode for a debit card purchase. As an example, the consumer made apurchase (or more likely repeated purchases) at Starbucks, and based onthe SIC code representing coffee retailer, the consumer may beclassified as a coffee drinker. As another example, a consumer may beassociated with an asset code via the account type information 13 withan SUV (the consumer has a car loan for an SUV). Based on this, theconsumer may be classified as an SUV driver. A taxonomy classificationcode is thereby assigned accordingly. It is to be appreciated also thatno single taxonomy classification system can cover every possibleclassification and, therefore, taxonomy classification systems aresubject to growth and evolution over time. The taxonomy classificationsystem 18 analyzes incoming bank consumer information and account-levelbank transaction data to deduce classification codes that are notprovided in the incoming data. For example, the incoming consumer andaccount-level data does not typically include whether a consumer is agrandparent or an SUV driver. These classification codes can however bededuced by analysis and correlation of the incoming transactions andconsumer information 14 for a given consumer.

It is to be appreciated that, although the merchant SIC code identifiesonly the merchant rather than the specific merchandise being purchased,arrangements can be made with specific merchants to obtain receipt-leveldata. In this case, the account-level transaction record information 10can be combined with receipt-level retail transaction data 32 in amatching process 34 to identify specific products purchased at themerchant location. Combining this information can advantageously enablepredicting future purchases based on the prior history of purchases. Forexample, when the account-level transaction record identifies that theuser has shopped at a hardware store, and the receipt-level dataindicates that the user has recently purchased drywall, drywall screws,and drywall compound, the system can automatically deduce that one ofthe consumer's next likely purchases will include sandpaper and/orpaint. Those are referred to herein as collateral purchases and are ofparticular interest to advertisers because of the high likelihood ofinterest from the consumer in advertisements that are correctly targetedtoward their next likely purchases.

The account-level bank transactions 10, the bank consumer information14, the bank account type information 13 and the retail transaction data32 can be further processed in an aggregate assumption phase 20. In theaggregate assumption phase, multiple account-level bank transactions fora particular consumer can be aggregated to make reasonable assumptionsabout the consumer's spending habits. Further, assumptions can bederived regarding annual income, when a raise is received, and whenannual bonuses might be received. Assumptions regarding annual incomecan be derived based on recurring bank deposits and so forth. Automobileloan or lease transactions can be aggregated to determine the length ofan auto loan or lease for the consumer. This sort of information can beparticularly useful for targeting advertisements to the consumer as theyapproach the end of a loan or lease period. Contract data, such as,e.g., wireless contracts, insurance contracts, and utility contracts canbe determined based on aggregation of the account-level banktransactions and retailer receipt-level data. Additional usefulinformation which can be aggregated includes, but is not limited to,annual travel habits based on change of merchant zip codes, what citiesare traveled, what time periods such travel is likely to occur, and soforth.

Assumptions deduced by the aggregate assumption phase 20 can be usefulin generating classifications 26 and classifying consumers intoclassifications. For example, suppose that an advertiser wants to targetindividuals who travel at least 100,000 miles per year. Aggregateassumptions about annual travel from the aggregate assumption phase 20can, based on trigger criteria, be utilized in a “Bubble-Up” process 22to classify particular consumers' as travelers of at least 100,000 milesper year. Similarly, persons belonging to a grandparent taxonomyclassification can be identified based on birth dates and data from theaggregate assumption phase 20 by examining purchases, over time, fromtoy stores. A person buying toys within the approximate same range ofmoney each time every year at toy stores, particularly if the person ispast age 60 for example, can be a good indication that the consumer is agrandparent. Further, this aggregate information can be useful inestimating grandchildren birthdays. And even further, based on the toysbeing purchased and the purchasing patterns, an estimation of thegrandchildren's age can be determined. This information Bubbling Up tothe classifications 26 can be very useful to advertisers for targetingadvertisements to these consumers at appropriate times during the year.

It is to be appreciated that the Bubble-Up process 22 is not necessarilya continuous process that occurs with each received account-level banktransaction, but rather, triggers can be established in the aggregateassumption phase 20 such that when enough information has beenaggregated to make the assumption useful as a classification, theinformation can then be Bubbled Up to the classifications 26. Referringagain to the hierarchical nature of the classifications, informationBubbling Up as a classification can continue from the lowest leveltouched by the information to higher levels in the classification,touching elements on the way up. For example, a consumer identified as amother can affects higher levels, i.e., female at the top level.However, the classification structure can be further described as beingpoly-hierarchical, i.e., a fork occurs when traversing theclassifications in an upward direction. As an example, a consumeridentified as a mother with a teenager, with aggregated transactionsincluding six transactions in the last 12 months over $100, and having adaughter can affect multiple higher levels.

The taxonomy classification codes developed in the taxonomyclassification phase 18 and/or transaction information are furtherprocessed in a complement identification phase 24. The complementidentification phase 24 applies analytics for determiningclassifications 26 related to collateral transactions. For example, foodpurchases may be indicative of someone likely to view movies. A new carpurchase could be an indication that car washes will be purchased morefrequently. A grandparent, would likely purchase toys on a grandchild'sbirthday. Similarly a trend identification phase 23 applies analyticsfor determining classifications 26 related to identified purchasingtrends. These identified trends are not necessarily obvious and are notdetermined based on obviousness, but instead are determined based onobserved trends. For example, the system may determine over time thatpurchasers of iPhone mobile phones are likely to purchase jeans. Theseidentified trends also contribute to the development of classifications.As with the aggregate assumption process 20, the complementidentification phase 24 and the trend identification phase 23 alsoutilize the Bubble-Up process 22 to Bubble-Up information into theclassifications 26.

The classifications represent information about a consumer related totheir lifestyle which is useful to advertisers for targetingadvertisements. The classifications also represent information about aconsumer that is useful to website publishers. Examples ofclassifications include identification as a grandparent, collegestudent, self-employed person, theater/movie attendee/watcher, investor,parent, and traveler. These examples, however, represent just a samplingof possible classifications used in embodiments of the presentapplication. It is to be appreciated that, similar to the evolution oftaxonomy classification codes, new classifications can be added overtime and evolve as the need arises. Some classifications may expire orbe removed over time, e.g., “married” becomes “divorced”, which can alsobe “single,” reinforcing the poly-hierarchal nature of theclassifications.

The classification of a consumer can also be weighted for trueness. Forexample, for the classification “grandparent,” a consumer is either agrandparent or not a grandparent—it is a true or false condition. Sincethe system does not know the identity of the consumer, and it isdesirable that the system does not know the identity of the consumer,the system cannot be 100% sure that the consumer is a grandparent. Forexample, even though a consumer's age is known to be over 60 years, thisconsumer may or may not be a grandparent. Using other information knownabout the consumer, e.g. transactions at toy stores near holidays,deposits into college savings investment vehicles, transactions atretailers selling young children's clothing, a weight, e.g. on a scaleof 1 to 10 or a percentage of trueness, can be assigned as to whetherthe consumer is to be classified in the “grandparent” classification.Even though the consumer is classified by the system as a grandparent,the consumer may not be a grandparent. That the consumer is not actuallya grandparent may not be that important to a marketer seeking to marketits goods and products to a grandparent. What can be important to amarketer is the fact that the consumer has the attributes of agrandparent.

Returning now to the trend identification phase 24, as previouslydiscussed, trend transactions can be useful in determiningclassifications such as grandparents and grandchildren's birthdays. Alsodetermined in the trend identification phase 24 are collateraltransactions which are useful for targeting advertisements. For example,a new car purchase may be an indicator useful for targetingadvertisements related to car care and car accessory equipment. It maybe useful to identify restaurant goers as potential movie attendees,particularly if they have a history of attending movies. Moreover,triggers can be set up to classify consumers based on life changingevents. For example, a moviegoer who has recently had kids can now beclassified in a DVD renter/purchaser classification.

With reference now to FIG. 2, a block diagram is shown for matching thereceipt-level retail transactions 32 to the bank transactions 10. Aspreviously discussed, the account-level bank transaction 10 includesinformation related to the transaction, however, it is a summaryincluding the total transaction amount and does not include individualline item detail, e.g., exactly what goods or services were purchasedfrom the retailer or service provider. For example, the bank transactionshown includes a merchant identification 40, an authorization code 42, atotal dollar amount 44, an industry specific code 46, a zip code 48 ofwhere the transaction transpired, and a date/time stamp 50. It is to beunderstood that the bank transaction 10 shown in the figure is exemplaryonly for purposes of describing the present application. Data providedin the account-level bank transaction 10 can vary based on whatindividual financial institutions furnish to the campaign generationsystem. For example, the bank transaction shown in the figure includesan authorization code 42, however, some financial institutions may notprovide the authorization code. The receipt-level retail transactiondata 32 can also be stripped of personal information belonging to theconsumer, e.g. the credit card or account number of the consumer makingthe purchase. This is desirable to protect the anonymity of theconsumer.

Like with the account-level bank transaction 10, the receipt-levelretail transaction 32 is not limited to the receipt-level data 33 asshown in the figure. The receipt-level data 33, as shown, includes amerchant identification 52, an authorization code 54, a total dollaramount 56, a zip code of where the transaction transpired 58, adate/time stamp 60, and line item data 62. The line item data 62includes one or more line item entries 63 wherein each of the line itementries 63 corresponds to a product or service purchased. In theexemplary line item data entry 63 shown in the figure, there is includeda product identifier 64, a quantity 66, and a line item price 68. Theproblem now is how to match the receipt-level retail transactions 32 tothe corresponding account-level bank transactions 10 in theidentification/matching step 34.

With regard to matching the receipt-level retail transactions 32 to theaccount-level bank transactions 10, there are basically two scenariosdescribed herein. In the first scenario, the bank transaction 10 caninclude a merchant identifier 40 and an authorization code 42, and thereceipt-level retail transaction 32 similarly can include a merchantidentification 52 and a merchant authorization code 54. Because eachmerchant can uniquely generate an authorization code for each retailtransaction, the merchant identification 52 and the authorization code54 can be used to readily match the receipt-level retail transaction tothe account-level bank transaction simply by matching the respectivemerchant identifications 40, 52 and the respective authorization codes42, 54.

In the second scenario, unlike in the first scenario, the authorizationcode is unavailable in either or both of the account-level banktransaction 10 and the receipt-level retail transaction 32. In thissecond scenario, matching analytics are used in the identification step34 to match the receipt-level retail transactions 32 to theaccount-level bank transactions 10. Various analytical comparisons canbe utilized by the identification step 34. For example, if theaccount-level bank transactions 10 and the receipt-level retailtransactions 32 each include total dollar amount 44, 56, zip code of thetransaction 48, 58, and a date/time stamp 50, 60, the respectivetransactions can be matched with a sufficient degree of certainty basedon matching these three pieces of information. It is to be understood,however, that exact matches of individual fields are not required tosuccessfully identify matching transactions. For example, it is to beexpected that the date/time stamp provided with the receipt-level retailtransaction 32 will not exactly match the date/time stamp of theaccount-level bank transaction 10. One reason for this is that some timemay transpire between the time the retail establishment generated adate/time stamp for the receipt-level retail transaction and the timewhen the financial institution applies a date/time stamp to the receivedaccount-level bank transaction for the corresponding retail purchase.Although the date/time stamp may not exactly match, it is only necessarythat it be close enough or within a given range so that, when combinedwith the other data, such as the dollar amount and zip code, there is areasonable degree of certainty that the transactions can be successfullymatched.

It is also anticipated that range comparisons may be appropriate forother data fields used by the identification step 34. For example,although the total dollar amount 44, 56 is typically provided as anexact amount, e.g., $3.17, some financial institutions offer programs totheir customers which may cause the total dollar amount to vary. Forexample, at least one financial institution is known to offer a servicewhere the total dollar amount is rounded up to the nearest whole dollarwhen the retail transaction information is received by the financialinstitution, e.g., $3.17 is rounded up to $4.00. The difference betweenthe two amounts is then deposited into a savings account for theconsumer. In this particular case, the total dollar amount of thereceipt-level retail transaction 32 and the account-level banktransaction 10 can be expected to match only to the nearest dollar.However, if the particular financial institution offering this programalso provides account-level transaction data for the savings account towhich the difference is deposited, the identification step 34 canutilize additional analytics to identify the savings account deposit ofthe difference and thereby improve the accuracy of the identificationprocess. Again, it is to be appreciated that the identity of theconsumer is neither available nor needed in performing theseidentification analytics. Therefore, the consumer remains anonymous tothe system, thereby assuring privacy regarding the consumer'sidentification.

Various other issues with respect to matching the account-level banktransaction 10 to the associated receipt-level retail transactions 32are envisioned and fall within the scope of the present disclosure. Forexample, although the respective merchant identifications 40, 52, eachinclude a specific merchant identification code, thereby enabling anexact match of the merchant identifiers, it is to be understood that thesystem can also match the merchants based on character descriptions ofthe merchants. As long as a merchant text description is provided forthe merchant identification 40, 52, the account-level bank transactionsand the receipt-level retail transactions can be matched at the merchantidentification level.

Turning now to the line item entries 63, once the receipt-level retailtransaction 32 has been matched to the account-level bank transaction10, the line item entries can be further analyzed and utilized by thesystem. While the account-level bank transaction 10 is useful indetermining classifications for the consumer, it is not possible todetermine the particular products or services purchased based on theinformation provided, except in the rare instances when the merchantonly provides one particular product or one particular service. The lineitem entry 63 is advantageously used for this purpose. For example, theline item entry 63 includes at least a product identifier 64, a purchasequantity 66, and a line item price 68. These data items provide theinformation which enables the system to determine and track products andservices purchased by the consumer. This product information can beuseful in determining purchasing trends of the consumer and can also beuseful in anticipating future purchases based on the products purchased.For example, as previously described, a consumer purchasing drywallmight be expected to purchase paint in the near future.

A problem arises, however, from the fact that various retailestablishments utilize their own internal product identification codes.Therefore, additional processing of the product identifier 64 isnecessary to recode the product identifier to a common coding systemutilized by the campaign generation system. Identifying and convertingthe product identifier code to a common code can be accomplished in avariety of ways. One method, is to utilize a retailer database 70provided by the retailer. The retailer database 70 enables the system tolook up a product based on the product identifier 64 and obtainadditional information regarding the product from the database table. Asshown in the figure, the product database may include, associated withthe product identifier 72, an SKU number 74, and various descriptivedata for the product, such as, e.g., available colors 76. The databaseinformation, such as the SKU number 74, can be used to determine ataxonomy code for the product. For example, the retailer may utilize aninternal taxonomy 78 for code defining their products.

It is to be appreciated, however, that the internal taxonomy 78 may be aproprietary taxonomy or an internal taxonomy utilized only by theparticular retailer and may, therefore, not be directly useable by theadvertisement campaign generation system for identification purposes. Itis to be understood that, while the internal taxonomy 78 is shown as aseparate entity in the figure, wherein the taxonomy code can bedetermined from information in the product database 70, it is alsoanticipated that the retailer database 70 may directly incorporate ataxonomy code for the particular product identifier 72. It is alsoanticipated that the internal taxonomy 78 may also be the same taxonomyutilized by the advertising system and, therefore, can be used directlyfor product identification. In the event that an internal taxonomy 78 isutilized, taxonomy codes from the internal taxonomy can be readilymapped to a common taxonomy 80 utilized by the campaign generationsystem.

It is to be understood that while the figure shows only one retailer, anumber of retailers can be associated with the campaign generationsystem and operate in a similar fashion. For example, a second retailerinternal taxonomy 82 is shown in the figure and can be similarly mappedto the common taxonomy 80. It is also to be appreciated that someretailers may utilize a separate third party taxonomy. This, however,only makes it necessary to map the third party taxonomy codes to thecommon taxonomy 80. It is to be further appreciated that there will notalways exist an exact match between the internal taxonomy 78 and thecommon taxonomy 80. For example, some in-house or internal taxonomiescan be sparse in cases where, e.g., the retailer utilizes broad-basedtaxonomy codes which cover a broad range of products. The hierarchicalnature of the common taxonomy 80 can be advantageously utilized in thiscase. For example, the lowest levels in the common taxonomy 80 describeproducts to a high degree of detail. These lower level taxonomy codes,like the limbs of a tree, branch off of a higher level taxonomy codewhich represents a broader range. This enables the sparse internaltaxonomy to be mapped to the common taxonomy 80, however, the mappingsimply occurs at a higher level, i.e., broader level in the commontaxonomy 80.

It is again to be appreciated that the identification process 34described with reference to FIG. 2 is an anonymous matching processprotecting the identity of the consumer. Even so, some financialinstitutions may desire to reduce the risk of personal identificationeven further. For example, a financial institution may only be willingto provide total transaction amounts 44 rounded to the nearest $0.10.This poses little problem to the matching process 34 because theanalytics described provide for comparisons based on ranges aspreviously described. To further enhance security, it is envisioned thatthe advertising system can provide for the installation of exportsystems installed in-house in a site owned or operated by the financialinstitution desiring the service. Providing an in-house export systemenables the financial institution to implement filters which ensure thatno personal identification information is exported from the financialinstitution. The in-house export system may, e.g., be configured toexport only known, non-sensitive data columns. The in-house exportsystem can be further configured to perform an analytical securityscanning of the data being exported as an additional level of security.For example, the system may be configured to monitor the outgoing datato ensure that no data, such as personal names, account numbers, orsocial security numbers are being inadvertently exported. An in-houseexport system gives the financial institution an added degree ofconfidence that the identity of their customers is protected.

With reference now to FIG. 3, a process is shown in summary form foradvertising campaign creation. This process could also be employed fordelivering other targeted media to viewers, not only advertisements. Atargeting phase 84 utilizes, but is not limited to, three processingcomponents to develop a targeted list 86. The targeting components oftargeting phase 84 include transactional metrics as previously discussedbased on account-level bank transactions, e.g., how much a consumerspends on their car which can be categorized within selected ranges. Thepreviously described classifications 26 are useful for targetingadvertisements as also previously described. However, the targetingphase 84 also performs analytics on classifications which areconditional, i.e., conditional classifications. Conditionalclassifications are those classifications which may be added to thetargeted list or removed from the targeted list based on performance asdescribed further below.

The targeted list 86 includes consumers targeted for a particularadvertising campaign based on analytics performed by the campaigncreation phase 84. Advertisements 88 are delivered to consumer on thetargeted list 86 on web pages 90 as the advertising campaign progresses.Advertisements can be delivered over other platforms, e.g. filecommunication systems, that may not be conducted with the Internet. Theadvertisements 88 can also be delivered to consumers who are not foundon the targeted list. As the advertising campaign continues feedbackfrom the advertising phase 88 is analyzed, e.g., based on click-throughrates, and utilized to generate a performance classification chart 92.The performance classification chart 92 is used to identify the topperforming click-through advertisements. Based on these click-throughmetrics, a redistribution phase 94 can redistribute the campaign targetsin order to yield better results, i.e., higher click-through rates. Tothis end, embodiments of the present application are able to monitor thedelivered targeted advertisements and determine when a click-through hasoccurred. The campaign redistribution feature can be an optional part ofthe advertisement campaign process. Advertisers can choose to have anadvertisement campaign automatically analyzed and redistributed based onperformance or to have the advertisement campaign remain fixed, butreceive reports indicating not only the performance of the campaign, butpotential performance based on a redistribution of the campaign.

It is to be appreciated, therefore, that embodiments of the presentapplication not only effectively target consumers for advertisingcampaigns based on classifications, but also dynamically redistributethe campaign targets for improved results during the lifetime of theadvertising campaign. For example, one of the classifications 26 mightbe related to zip codes in the north east section of the United States.Therefore, based on zip code a classification can be created for thenorth east. During the advertising campaign, the performanceclassification 92 may indicate a much higher click-through rate for thenorth east classification as opposed to the remainder of the country.So, based on this, the advertising campaign can be redistributed orretargeted to show increasing advertisements to those people in thenorth east and fewer advertisements in other areas of the country. Itmay be possible, therefore, to target additional consumers in the northeast based on the north east classification which would have otherwisebeen missed in the original advertising campaign. Furthermore, if theadvertisements are being delivered to consumers not found on thetargeted list for the campaign, but the many consumers belonging to aparticular classification not part of the targeted list click on theadvertisement, then the advertisement can be redistributed to consumerswho fall within the classification that are clicking on theadvertisement.

It is to be appreciated that any data collected or mined in embodimentsof the present application, regardless of the source, can be used togenerate classifications. Retailer receipt data, or additional data fromthe retailer, browsing history, search history, and advertisement clickhistory, to name a few, can be used to generate classifications.Preferred embodiments are able to collate this information in order toprovide a more complete picture of the consumer. It is desirable,although not required, that the additional data still protect theanonymity of the consumer. The ADIC and the UCIC, being a unique butanonymous identification of the consumer, facilitates the collation ofthis consumer information.

With reference now to FIG. 4, a flow chart of an embodiment of thepresent application is shown. It is to be understood that each of thesteps in the flow chart corresponds to processing as describedpreviously with reference to FIG. 1. Therefore, the flow chart isdiscussed in a block summary form, without unnecessarily repeatingdetail previously described. It is also to be appreciated that the flowchart is provided for understanding embodiments of the presentapplication, however, the present application is not limited to thearrangement of steps shown in the figure. At step 100, an advertisingcampaign and delivery system receives bank consumer information fromfinancial institutions, the consumer information including consumeridentification and consumer related data. The consumer is identifiedanonymously by association with a unique customer identification code,UCIC, associated with the consumer. The received consumer information isfurther utilized by a classification maintenance and storage process 102which maintains classifications for use by an advertising campaigncreation procedure step 104.

The received consumer information is also used by a taxonomyclassification procedure 116 which determines taxonomy classificationcodes based on the received consumer information. The taxonomyclassification, however, is additionally based on account-level banktransaction data received in step 110 from financial institutions, theaccount-level bank transaction data including the anonymous consumeridentifier and merchant identifiers and transaction data related to thetransaction the consumer conducted with the merchant.

The received account-level bank transactions are further utilized by anaggregated transaction procedure 112 which identifies transactionsassociated with a particular consumer and aggregates them in order togenerate the previously described aggregate assumptions. At step 114,based on aggregation triggers, the aggregate assumptions are provided,i.e., bubbled up, in the form of classifications to the classificationmaintenance and storage process step 102. The taxonomy classificationsare further utilized by collateral and trend identification proceduresat step 122 to identify classifications based on collateraltransactions, timed transactions, and identified trends. Further, basedon triggers, key lifestyle information identified by the collateral andtrend identification procedure 122 are bubbled up at step 124 to theclassification maintenance and storage system step 102. As needed, theclassifications maintained and stored by the classification maintenanceand storage procedure 102 are provided to an advertising campaigncreation process step 104.

With reference now to FIG. 5, a flow chart is shown describing a methodof matching bank-level transactions to receipt-level transactions andidentifying line item products and services in the receipt-leveltransaction. Account-level bank transactions are received in step 130.The account-level bank transactions are received in a manner previouslydescribed with reference to FIGS. 1 and 2. It is to be understood thatthe system is configured to receive the account-level bank transactionsthrough a variety of processes and in a variety of formats. For example,some financial institutions, as previously described, may have in-houseexport systems installed to ensure that only non-sensitive data isprovided to the campaign generation. These in-house export systems canformat the data into a format suitable for the campaign generation. Onthe other hand, some financial institutions may desire, for the sake ofreduced processing overhead, to simply provide raw data to the campaigngeneration. In this event, the campaign generation is programmed to mapthe desired non-sensitive information to the requirements of thecampaign generation and protect the anonymity of the financialinstitution customers by not accepting any personally identifiableinformation. It is anticipated that a variety of filter andtransformation algorithms can be provided by the campaign generation inorder to accommodate a variety of financial institutions, each havingdifferent requirements.

Receipt-level retail transactions are received in step 132. Thereceipt-level transactions, similar to the account-level banktransactions, may be processed through various filters andtransformations to reformat the transactions from various formats usedby the various retailers into a common format utilized by the campaigngeneration system. It is also envisioned that retailer export systemsmay be installed in-house with the retailer for the purpose offormatting and/or ensuring the anonymity of the retail customers. If amerchant identification code and authorization code, as determined atstep 134, are available in each of the received account-level banktransactions and the receipt-level retailer transactions, thetransactions can be matched based on the provided merchantidentification code and the authorization code at step 136. Otherwise,as previously described with reference to FIG. 2, analytic processing atstep 138 can be utilized to match the transactions based on, e.g.,transaction dollar amount, zip code of the transaction, and a date/timestamp.

When the receipt-level retail transaction has been successfully matchedto an account-level bank transaction, the individual line item detailsof the receipt-level transaction can now be further processed at step140. In step 142, the individual line item product can be identified bymatching the product ID provided in the line item entry to a retailerdatabase utilizing the product ID as a search key. From data provided inthe retailer database, at step 144, the product ID can be translatedinto either one of a common taxonomy, an in-house retailer taxonomy, ora third party taxonomy. In the event that the product ID is matchedeither to an in-house retailer taxonomy or a third party taxonomy, theresulting taxonomy code can be further mapped at step 146 to the commontaxonomy code utilized by the advertising system.

With reference now to FIG. 6, a flow chart is provided describing theadvertising campaign creation procedure 104 as shown in FIG. 4. Aconsumer targeting step 150 targets consumers based on transactionalmetrics computed based on the received account-level transaction dataand, in some embodiments, also based on received receipt-level datareceived from participating merchants. Consumers are also targeted basedon the stored classifications and are further targeted based onconditional classifications as previously described. At step 152, atargeted consumer list is generated based on the consumers determined inthe consumer targeting step 150. These targeted consumers are identifiedanonymously by their associated ADIC or UCIC as previously described. Atstep 154, targeted advertisements are provided to the advertisementdelivery system for consumers in the targeted list. At step 156,advertisements provided by the advertisement delivery system aredisplayed as website content viewed by the targeted consumers. Contentother than advertisements can be displayed on devices that are connectedto a network other than the Internet. Also at step 156, advertisementscan be delivered to consumers, e.g. a random set of consumers, who arenot on the targeted list. At step 158, the advertisement delivery systemcollects data related to the number of advertisements displayed andassociated click-through data for performance evaluation. Thisperformance evaluation is fed back to the targeted list generation step152 which further provides this information to a classificationperformance ranking step 160. In this step, the click-throughperformance related to the stored classifications is analyzed and, atstep 162, the targeted list is redistributed based on deletion ofunderperforming classifications and addition of newly identifiedclassifications that show a high likelihood of a beneficialclick-through rate. For example these new classifications can beidentified from the consumers who were not on the targeted list, butreceived the target advertisement meant for the targeted list andclicked through the advertisement. If the ADIC is known for the randomcustomers who clicked on the targeted advertisement, then the ADIC canbe matched to the classifications in which the consumer is categorizedto find new consumers in classifications that were not originallytargeted by the advertisement.

Additionally, the system and method can allow an advertiser/marketer tocreate a broad advertising campaign and the system and method canevaluate the performance of the campaign in a similar manner to theclassification performance ranking step 160, above. The system willidentify the best performing classifications based on the click throughrate and then redistribute targeted advertisements to consumers in thebetter performing classifications.

The advertising campaign creation system can also be employed, withslight modifications, to restructure a web site, or other contentdisplayed on a networked device, based on classifications in which theviewer of the content falls into. For example, if the viewer is in aclassification indicating that he resides in Florida, then the weatherfor his region can be automatically delivered for display on the device.If recent transactions have taken place in another region of thecountry, then a website can deliver content, e.g. news and weather,based on that region.

The system is useful in delivering targeted content other thanadvertisements. Additional examples include delivering news articles,audio/visual and other content based on classifications andtransactional metrics that would interest the consumer when he visitsthe website. For example, if the viewer of espn.com falls into theclassification “golfer,” then espn.com could open with a golf article asopposed to its more generic article that opens for others who visit thesite. The system checks for the ADIC, which can be in the form of apersistent cookie, stored on the device that is loading and/or viewing awebsite, e.g. espn.com. In sum, the classifications and transactionalmetrics can be used to deliver content, other than advertisements, to adevice. Such a system and method for delivering advertisements andcontent can enhance a viewer's session, e.g. on the website, which canresult in the viewer wishing to return more often to view the content.The advertisements can be further personalized based on theclassifications and other information that is known about the consumer.This will be described in more detail below.

With reference now to FIG. 7, the interrelationship between those whowish to deliver targeted content and those who will receive the targetedcontent will be described. The system allows for communication among thefollowing individuals or entities: marketers 202, financial institutions204 and consumers 210. The financial institutions 204 can include banks,savings and loans, credit unions, and the like. As previously described,each of the financial institutions may include an in-house export system206 for exporting and/or filtering information and a secure database 218(or a plurality of secure databases) that is/are operated by thefinancial institutions 204 that stores, or warehouses, the consumerinformation and financial transactions (and other financial information)of the customers of the financial institution along with othernon-financial information. These financial transactions can include thedebits and credits of the customers of the bank, the loans that are heldby the bank for that customer, credit/debit card transactions and thelike. The other information about the consumer that is stored in thefinancial information secure database 218 includes the consumerinformation such as the identity of the consumer, the age and sex of theconsumer and the home zip code of the consumer. This consumerinformation is associated with a unique consumer identification code(UCIC) that associates the consumer to the information while stillmaintaining the anonymity of the consumer. By anonymity is meant thatthe information communicated to the advertising delivery providerprecludes the provider from knowing who the consumer really is so thatthe “cookie”, which can be later presented to the consumer, isanonymous. Accordingly, the UCIC can be referred to as an anonymouscoding. For example, the UCIC is not based on the name, address, e-mailaddress, phone number, a government issued identification such as asocial security number or the account number of the consumer at thefinancial institution, which could lead to the identity of the consumerbecoming known.

The UCIC is tied to the financial transactions of the consumer, the ageand sex of the consumer, and the zip code of the consumer; however, morepersonal information, such as the social security number, phone number,credit card numbers and the name of the consumer, is not associated withthe UCIC, thus protecting the identity of the consumer. The UCIC codesare communicated with the consumer information through an interface orportal, referred to as a non-consumer portal 220, to the processor 216.The advertisement delivery provider database 214 associates the UCICwith the information that is similar to that stored in the financialinstitutions databases. The advertisement delivery provider database 214stores, or warehouses, the financial information and other non-personalinformation that it receives from a number of different financialinstitutions. The advertisement delivery provider database 214 alsoassociates an advertisement delivery identification code (ADIC) and afinancial institution identification code (FIIDC) for each individualconsumer stored in its database and associates these codes with the UCICthat is provided by the financial institution database. The ADIC isunique to each consumer stored in the database. The ADIC can also beassociated with the device that is used to access content over thenetwork, which can allow a single ADIC to be associated with more thanone UCIC and FIIDC. Moreover, more than one ADIC can be associated withone UCIC. The FIIDC is associated with the financial institution thathas the provided the consumer information for the unique consumer. Sincethe UCIC maintains the anonymity of the consumer to which it is matched,the ADIC and the FIIDC also maintain that anonymity of the consumerbecause no personal information is matched to these codes. Accordingly,the UCIC and the ACIC can also be referred to as anonymous codings. TheADIC is assigned by the operator of the advertisement delivery providerdatabase 214 to the consumer 210 or to a specific device operated by theconsumer and utilized thereafter in communications between the consumer210 and the advertisement delivery provider for anonymous uniqueidentification of the consumer. The ADIC is further communicated to thecampaign generation system 216 so the system can correlate and combinedata for the consumer from multiple of the financial institutions 204.

The system can also allow companies or entities that are not financialinstitutions to allow for the delivery of advertisements on their websites or other communication platform—these entities will be referred toas third party advertisement presenters 212. The marketers 202 arecompanies or individuals who wish to deliver targeted advertisements totargeted consumers 210. The consumers 210 are also customers of at leastone of the financial institutions 204 that share information within thesystem. The consumer information, e.g., birth date, zip code, etc., andconsumer account-level transaction data, which is provided by thefinancial institutions, is used to determine classifications stored inan advertisement delivery provider database 214 maintained by a campaigngeneration system 216 operated by the advertisement delivery provider.The third party advertisement presenters 212 operate web sites or otherpublication outlets that are not affiliated with the any of thefinancial institutions (or are unsecure web sites that are operated bythe financial institutions) that allow for the delivery ofadvertisements. The system is designed to maintain the anonymity of theconsumers while allowing the marketers to have their advertisementsdelivered to consumers who fall within their defined market segment.Additionally, the system allows for communication to the third partyadvertisement presenters 212 (or other third party publishers ofcontent) to receive messages that instructs the presenter 212 to publishcontent based on at least one of the classifications associated with theconsumer viewing the webpage.

Generally the system incorporates an embodiment of the methods describedwith reference to FIGS. 4-6. The targeted advertisements based on theclassifications of the consumers 210 are delivered to the consumer whenthe consumer visits websites 222 (or other publication displays orapplications) having an association with the financial institutions 204or third party advertisement presenters 212. The targeted content, oneexample being advertisements, can be delivered either directly to theconsumer 210 or via the visited websites or other applications 222. Itis intended that the advertisement be able to be delivered to anydevice, e.g. computer, mobile phone, television set, that is able tostore a persistent cookie, or other persistent unique identifier.However, if an advertisement is being displayed on a secure applicationoperated by a financial institution, targeted content can be deliveredwithout having to set a persistent cookie.

With reference to FIG. 9, at 230 the content viewer, who is also acustomer of one of the financial institutions, logs onto the financialinstitution's website or securely enters an application that is operatedby or associated with the financial institution. The consumer logs ontoa protected portion of the financial institution's website orapplication where the consumer must identify himself appropriately sothat, for example, the financial institution allows the user to performbanking transactions over the Internet or other network. At 232, thefinancial institution passes the UCIC associated with the content viewerfrom the financial institution to the processor 216 (FIG. 7) operated bythe advertisement delivery provider, which has also been referred to asthe targeted content provider. At 234, the targeted content providerassociates and ADIC with the received UCIC. The advertisement deliveryprovider can check the advertisement delivery provider database 214(FIG. 7) for an ADIC associated with the received UCIC. If no ADIC isassociated with the received UCIC, then at 236 the advertisementdelivery provider can assign an ADIC to this device and set a cookie onthe device that includes the ADIC or other similar identification. Sincean ADIC has not been associated with this device, or this device haserased previous cookies including the ADIC, not enough information isknown about this device to send targeted content, e.g. an advertisementfor display on the device. Accordingly, at 238 a non-targetedadvertisement can be delivered at this time to the device.

With reference back to step 234, if an ADIC is associated with thereceived UCIC and the ADIC cookie is present on the device, then at 240the advertisement delivery provider checks to see if the ADIC cookiethat is present on the device matches the UCIC that is associated withthis ADIC. If the ADIC matches the UCIC, then enough information isknown about this device to send targeted content, because the financialinformation associated with the UCIC can be used to determine theclassifications into which the consumer using the device falls into.Accordingly, at 242 a targeted ad, or other targeted content, can bedelivered to the device.

With reference back to step 240, if the ADIC cookie stored on the devicedoes not match the UCIC associated with that ADIC, then at 246 theadvertisement delivery provider can record the association of the ADICstored on the device (via the persistent cookie that has been previouslyset) with another UCIC. This can happen for example, where the device isused to log onto two different financial institution applications. Whenlogging into the first financial institution application, theadvertisement delivery provider can assign an ADIC associated with theUCIC from the first financial institution. If this device is then usedto log onto another secure application of another financial institution,then an ADIC will have already been stored on the device, but the UCICfrom the new financial institution will not match the previouslyreceived ADIC. After recording the association of the ADIC with theanother (different) UCIC stored at 246, then at 248 the advertisementdelivery provider can query whether the another (different) UCIC and theADIC have been identified together enough to meet a threshold. If thisanother (different) UCIC and the stored ADIC have been identifiedtogether enough to meet a threshold, then enough information is knownabout the device to associate this another UCIC with the ADIC, thereforea single ADIC can now be associated with two UCICs and the financialinformation from two different financial institutions can be associatedwith one content viewer or device operated by a content viewer.Accordingly, enough information is known about the content viewer usingthis device that a targeted advertisement can be delivered. If the UCICand the ADIC have not been identified together enough to meet athreshold, then a non-targeted advertisement can be delivered.

Analytics can be used to determine whether the device that is being usedis a public computer. For example, if many different UCICs areassociated with the same ADIC, then an assumption can be made that thisdevice is being used by members of the general public and thereforegeneral advertisements can be delivered to this device. However, if thesame UCICs and the ADIC have been associated together many times, it islikely that this device is used by the same person who is a customer ofdifferent financial institutions. Accordingly, the financial informationavailable from these different institutions can be associated with thesame ADIC. The analytics used to determine whether the UCIC and the ADIChave been identified together enough to meet a threshold will depend onthe account type. For example, a consumer will likely not check thefinancial institution's application where the financial institutionholds a car loan for the consumer as often as the consumer will checkhis or her checking account. Accordingly, the UCIC associated with thecar loan account, may not have to be associated with the same ADIC asmuch as a checking account would in order to meet the thresholddescribed at step 248.

FIG. 9 depicts one method for delivering targeted content to a deviceover a network. FIG. 9 describes how the method includes identifying aconsumer who is viewing content based on a unique identifier stored onthe device used by the consumer to view the content. For the methoddescribed in FIG. 9, the unique identifier is a persistent cookie thatcan be set on the device used by the consumer to access the content.FIG. 11 depicts another method for delivering targeted content to adevice over a network and also employs an unique identifier associatedwith the device used by the consumer to view the content. FIG. 10describes a method for associating the unique identifier with thefinancial information of the consumer who uses the device.

With reference to FIG. 10, at 330 the content viewer, who is also acustomer of one of the financial institutions, logs onto a website orother application that is operated by the financial institution. Theconsumer logs onto a protected portion of the application where theconsumer must identify himself appropriately so that, for example, thefinancial institution allows the consumer to perform bankingtransactions over the network. At 332, the financial institution passesthe UCIC associated with the content viewer who has just logged into thesecured financial institution application to the targeted contentprovider. Another way of stating this is that the targeted contentprovided receives the UCIC. In this example, the UCIC is passed to theprocessor 216 (FIG. 7) operated by the advertisement delivery provider.At 334, the advertisement delivery provider captures the IP address forthe device used to log onto the secure financial institutionapplication. To protect the identity of the consumer who is using thedevice to view the secure portion of the financial institutionapplication, the IP address is hashed using a cryptographic hashfunction, e.g. MD5 and SHA-1. The IP address can only remain temporarilyin the server memory of the advertisement delivery provider, and then belet go from the memory of the advertisement delivery provider's server.To further protect the identity of the user of the device, the IPaddress may not be written to a disk or stored in a database operated bythe advertisement delivery provider. The hash value for the IP address,also referred to herein as the hashed IP address, can be the onlyidentification associated with the device that is stored by theadvertisement delivery provider. Since it can be extremely difficult ornearly impossible to calculate a text, e.g. the IP address, that has agiven hash the IP address for the device used to access the secureapplication of the financial institution is not known to theadvertisement delivery provider. At 336, the hashed IP address is storedin a database operated by the advertisement delivery provider and at338, the association of the hashed IP address with the UCIC receivedfrom the financial institution is recorded. Accordingly, the device thatis used by the operator can be matched with the financial information ofthe operator, but the IP address of the device used by the operatorremains anonymous or unknown to the targeted content delivery providerdue to the hashing of the IP address. In addition to cryptographichashing, other encryption functions and algorithms can be applied to anIP address so that the IP addresses used to access financial institutionapplications are not stored on the server of the advertisement deliveryprovider.

FIG. 11 depicts a method for delivering targeted content to a deviceover a network using the hashed IP address of the device used to accessthe network. At 350, the consumer views a networked application, e.g. awebsite (the website need not be associated with a financialinstitution). At 352, the advertisement delivery provider captures theIP address for the device used to view the networked application. At354, the IP address is hashed, e.g. subjected to a cryptographic hashfunction, and let go from a server operated by the advertisementdelivery provider. Similar to what has been described above, in thisexample the IP address is not written to any database or disk under thecontrol of the advertisement delivery provider.

At 356, the advertisement delivery provider determines whether thehashed IP address is associated with a UCIC stored in its database. Ifthe hashed IP address is not associated with a UCIC in the database ofthe advertisement delivery provider, then at 358 not enough informationis known about the operator of the device to deliver targeted contentand therefore non-targeted content is delivered to the device. Ifhowever, the hashed IP address is associated with a UCIC at step 356then at 362, the advertisement delivery provider determines whether thehashed IP address has been associated with multiple UCICs. For example,for the device that is being used to access the networked applicationmay be a public computer and many different UCICs can be associated withthe same hashed IP address. Analytics can be used to determine whetherthe device that is being used to access the networked application is apublic computer. For example if many different UCICs are associated withthe same hashed IP address, then an assumption can be made that thisdevice is used by members of the general public.

At 362 if the hashed IP address has not been associated with multipleUCICs, then enough information is known about the operator of thedevice, since a single hashed IP address has been matched to a singleUCIC, and therefore the financial information associated with theconsumer that matches the UCIC can be associated with the hashed IPaddress. This allows for targeted content to be delivered to the deviceat 356. If, however, the hashed IP address has been associated withmultiple UCICs at 362, then at 366 the advertisement delivery providerdetermines whether the associations between this hashed IP address andthe multiple UCICs is typical. For example, if the same device is beingused to check a mortgage, a car loan, a checking account, and a savingsaccount, then all of these accounts may be owned by the same individualand it can be assumed that the individual who is operating the devicethat is checking these accounts is the owner of each of these accounts.Accordingly, the financial information associated with each of theseaccounts can be tied back to a unique identifier for the consumer, e.g.the hashed IP address of the device, and targeted content can then bedelivered to that device at step 364. However, if the associationsbetween this IP address and the multiple UCICs are not typical, forexample hundreds of different checking accounts have been associatedwith the same IP address, then this device associated with this IPaddress may be used by the general public and therefore the delivery oftargeted content based on financial information of one of the consumerswho operates the device would be difficult. Accordingly, non-targetedcontent can be delivered at step 358.

With reference to FIG. 8 an advertisement can be personalized to theconsumer by augmenting a base advertisement 300, which is based on theproduct or service that is being offered by the marketer or advertiser,with an augmentation 302 that is based on the classifications, the bankconsumer information, the account type information or the banktransaction information to deliver a blended advertisement 304. Anaugmentation of a base advertisement for a car will be provided as anexample. For example, if the consumer falls into the “golfer”classification and the advertisement that is to be delivered is for acar, then an image of the car that is being advertised can besuperimposed onto an image of a golf course. The blended advertisement304, which is this example is the car superimposed on a golf course, isthen delivered to the consumer's device, e.g. computer, mobile phone, orother device that is able to store a persistent cookie. Another mannerof stating the personalization of an advertisement is that a portion ofthe content of an advertisement is based on the classification or otherinformation that is known about the customer. The superimposition of oneimage, e.g. the car, over another image, e.g. a golf course, wouldresult in separate images appearing to the viewer of the advertisementas a single image, which is made up of the two images blended together.

The selection of the image that is based on a classification, i.e. theaugmentation 302, need not directly correlate to the classification inwhich the consumer is categorized. For example, the background color,which would be the augmentation, of the blended advertisement may bedetermined by a classification of the viewer of the advertisement. Forexample, a green background can be chosen for an advertisement of aproduct to persons who are in a classification “environmentalist” eventhough the product may not be associated with the environment, e.g. acomputer.

Moreover, the multiple classifications that the consumer viewing theadvertisement falls into can be weighted to determine the content of theadvertisement. For example, where the consumer is classified in both the“golfer” classification and the “grandparent” classification, thecontent of the augmentation, e.g. the background image of theadvertisement, can be a function of values given to differentclassifications. Values can be assigned to certain classifications sothat if the consumer falls into different classifications, then theclassification having the higher value is matched to the content for theadvertisement. In the example of a car advertisement, the “golfer”classification can be assigned a higher value than the “grandparent”classification so that an image associated with a golfer is displayedalong with the advertisement as opposed to an image associated with agrandparent.

The content of the advertisement can also include audio or video contentthat is based on the classification of the consumer viewing or listeningto the advertisement. For example, with reference back to a caradvertisement, audio content can be tailored so that consumers fallinginto the classification “college student” hear music popular on collegeradio stations and consumers falling into the classification “orchestraattendee” hear classical music, but the same car would be viewed foreach advertisement.

The content of the advertisement can also be a function of the retailtransaction data 33 (FIG. 2) for a specific consumer. For example, if itis known that a consumer recently purchased brand X coffee, then anadvertisement for brand Y coffee can be delivered to the consumer.Similarly, the retail transaction data can be helpful in deliveringtimely and geographically relevant advertisements. For example, if it isknown that a consumer recently paid for a dinner at a restaurant, thenadvertisements can be delivered to the consumer's mobile device fordesserts and other complementary products, e.g. coffee or movie tickets,for locations near the restaurant where the consumer dined. Thistechnology allows for these timely delivery of advertisements withoutthe need for global positioning devices to know the location of theconsumer—instead the receipt level data, which shows the location of therestaurant can provide the geographic information needed to provide thegeographically relevant advertisement.

The content of the advertisements delivered can also be morespecifically based on the account type information 13 (FIG. 1) for theindividual receiving the advertisement. For example, if it is known thatthe viewer of the advertisement has a car loan that is about to be paidoff, e.g. there are less than about three or four car payments remainingon an installment loan, then the advertisement that is delivered to theviewer can include information about the current loan and an offer for anew car. For example, the advertisement could read: “You have threepayments remaining for $356.75 per month for your 2004 Honda Accord, wecan get you into a 2009 Acura TL for $415 per month.”

The content of the advertisements can also include offers for productsand services from different marketers within the same advertisement. Forexample, the advertisement, “You have three payments remaining for$356.75 per month for your 2004 Honda Accord, we can get you into a 2009Acura TL for $415 per month,” could involve a car dealership and a bankpartnering together to offer the car and an installment loan.

The content of the advertisements delivered can also be based on banktransaction data 10 (FIG. 1). For example, if the bank transaction dataindicates that a consumer is paying $100 per month to natural gasprovider X, then an advertisement that can be delivered to the consumerthat includes information about this data. For example, an advertisementdelivered to the viewer could read: “You pay $100 per month for naturalgas to company X, switch to company Y and pay $80 per month.”

With reference to FIG. 12, an example of a method for processing a listof customers to receive targeted content is shown. At 400 financialrecords for customers of at least one financial institution arereceived. The financial records will typically be received as batch datafiles delivered over a computer network. This is explained above withreference to FIGS. 1 and 2 where the financial records can include bankcustomer information (also referred to as bank consumer informationabove), which can be anonymous demographic data about a customer as anindividual such as, e.g. birth date, gender, zip code, etc. The receivedfinancial records can also include bank account type information, whichcan include a type of account held by the consumer, e.g., savings,checking, mortgage, IRA, credit card account, asset loan, etc. This bankaccount type information can also include information related to eachaccount, as applicable, such as inception date, terms for mortgages orcertificates of deposit, payment amounts for mortgages or other loans,and so forth. This bank account type information need not include theaccount number for the customer, which can further protect the privacyof the customer. The financial records can also include bank transactiondata, which can describe individual transactions conducted by thecustomer such as, e.g., a credit/debit card purchase at a retaillocation.

At 400, unique identifications (“IDs”), which can be assigned by thefinancial institution that is providing the financial records, can alsobe received. These unique IDs can be similar to the UCIC describedabove. These unique IDs received at 400 can be anonymous in that thefinancial institution customer who is associated with the unique ID maynot be identified by the received unique ID, e.g. the unique ID is notassociated with a name, a bank account number, a street address, ane-mail address, a social security number or a phone number for therespective customer. Alternatively, the unique ID may be non-anonymous.

At 402, a determination is made as to whether each unique ID that hasbeen received is anonymous. As mentioned above, the financialinstitution may deliver the financial records for its customers wherethe financial information is associated with a unique ID that is notanonymous. If the received unique IDs are not anonymous, then at 404these non-unique IDs can be encrypted. Known encryption software can beused to encrypt the non-unique IDs. If the unique IDs are anonymous,then at 406 the received financial records of each consumer can beassociated with an anonymous identification code, where each anonymousidentification code is representative of a respective consumer. Theanonymous identification codes, which can be similar to the ADICs(described above), can be assigned by the system, and more particularlyby software running on the processor 216 in FIG. 7.

With reference back to step 404 where the unique IDs are encrypted, theprocessor 216 depicted in FIG. 7 can also include software to encryptthe non-anonymous unique IDs that have been received by the processor.At 408, the non-anonymous received unique ID can be released where thenon-anonymous unique ID is not written to any database or memory underthe control of the operator of a database that stores the financialrecords and the anonymous identifiers that are representative of thecustomers. The encrypted unique ID, since it is now anonymous, can beassociated with the consumer's financial records and operate similar toan ADIC, which has been described above. In other words, at step 406 anADIC can be associated with each anonymous UCIC provided by a financialinstitution, or the encrypted UCIC, which is now anonymous, can beassociated with the financial records of the consumer that was oncerepresented by a non-anonymous UCIC.

The system in FIG. 7 (or another similar system) can also match acustomer who has different accounts at different financial institutionsto the same anonymous identification code. For example a consumer, whowill be referred to as a multi-financial institution customer, can haveaccounts at different financial institutions. The database can receivefinancial records for a multi-financial institution customer where themulti-financial institution customer is a customer of at least twodifferent financial institutions. Software and/or hardware on theprocessor 216 (FIG. 7) can associate the received financial records froma first financial institution with a first anonymous identificationcode, e.g. a first ADIC. Software and/or hardware on the processor 216(FIG. 7) can also associate the received financial records from a secondfinancial institution with a second anonymous identification code, e.g.a second ADIC. The system can be configured to track financialtransactions where the multi-financial institution customer transfersmoney between the first financial institution and the second financialinstitution. Based on having tracked a predetermined number of financialtransactions where the multi-financial institution customer transfersmoney between the first financial institution and the second financialinstitution, the system can identify the multi-financial institutioncustomer represented by the first anonymous identification code and themulti-financial institution customer represented by the second anonymousidentification code to be the same customer. Accordingly, thismulti-financial institution customer can be represented by the sameADIC, e.g. either the first ADIC or the second ADIC, or a newlygenerated ADIC.

At 410, the anonymous unique IDs, the anonymous identification codes andthe financial records are stored in a database, such as the database 216depicted in FIG. 7. With reference to the embodiments described above,the ADICs and the UCICs could be stored along with the financial recordsof the consumer. These can be stored in a database, such as the database214 depicted in FIG. 7.

At 412, a request can be received to generate a targeted list ofconsumers who meet selected criteria. This has been described with muchdetail above. For example, a financial institution may be interested indirecting targeted content, e.g. advertisements, to consumers who are inthe market for a home equity loan. This request can be received fromentities including the following: marketers/advertisers 202 (FIG. 7),financial institutions 204 (FIG. 7) or even third party ad presenters212 (FIG. 7).

At 414, the database is queried to identify consumers based on theselected criteria and the associated financial records associated witheach consumer. This has been described in much detail above. Forexample, the consumers based on the financial records can be classifiedinto classifications, e.g. “student,” “grandparent,” or even “individuallikely to require a home equity loan.” These classifications can begenerated using the methods and systems described above; therefore,further explanation as to how to identify consumers who meet selectedcriteria is not provided.

At 416, the numerous amount of data stored in the database istransformed into a targeted list of customers based on the consumersidentified when querying the database and each consumer's correspondingfinancial records. The possibly millions of stored anonymousidentification codes and the accompanying financial records stored inthe database can provide the basis upon which a targeted list ofcustomers can be generated where customers meet the selected criteria.The targeted list of customers identify each customer using an anonymousidentifier that is representative of a consumer of the at least onefinancial institution but does not personally identify the consumer. Thegenerated targeted lists can be stored in the database, or the generatedtargeted lists can be displayed, e.g. printed on paper or displayed on acomputer screen. This transformation of numerous financial records fornumerous consumers into a list of consumers who are an appropriateaudience for highly targeted content is very useful to publishers ofsuch content. For example, publishers of content other thanadvertisements can tailor content, e.g. news articles, instructionalmanuals, to individuals based on the lifestyle of the individual, whichcan be deduced from the financial records for that individual.

At 418 it is determined whether the targeted list that was generated at416 includes any encrypted unique IDs. If the targeted list includesencrypted unique IDs, then at 420 a key is provided to the entityreceiving the targeted list. This key allows the entity receiving thetargeted list to match the encrypted unique ID to a non-anonymous uniqueID by which the consumer can be identified by the entity receiving thetargeted list. The targeted list of customers is then delivered at 422.This targeted list of customers can be used as an audience to providetargeted content. The targeted content is not limited to only deliveryvia the internet or over another network. For example, if the entitythat receives the targeted list knows the residential address of theconsumer, postal mail can be delivered to the consumer that includes thehighly targeted content. Moreover, if the entity that receives thetargeted list knows the phone number of the consumer, a telephone callcan be made to the consumer on the list. The entity that generates thetargeted list at 416, however, never knows the identity of the consumer.

A system for processing a list of customers to receive targeted contentcan include the system that was described above and is more clearlydepicted in FIG. 7. The system can include a database, such as database214 shown in FIG. 7, that stores anonymous unique identifications for aplurality of customers of at least one financial institution. Thedatabase can also store financial records for the customers. Asdescribed above, each anonymous unique identification can berepresentative of a customer of the at least one financial institutionand be associated with the financial records for the respectivecustomer. The system can also include a processor, similar to theprocessor 216 shown in FIG. 7. The processor can include softwareconfigured to classify the customers into selected classifications basedon the financial records associated with each anonymous uniqueidentification. The processor can also be configured to create a list oftargeted content viewers based on at least one selected classification.The list can include at least one anonymous unique identificationassociated with the at least one selected classification. Since thesystem has been described above in more detail, further description ofthe system for processing a list of financial institution customers forreceiving targeted content is not provided.

With reference to FIG. 13, a system 510 is illustrated that facilitatesnotifying a customer of a targeted offer, reward, or other message whenthe customer calls in to the business (e.g., a financial institution)and is placed on hold and/or navigates an automated menu presented by avoice response unit (VRU). In one example the message relates to accountinformation for the customer's account, such as a message regardingopting in to an overdraft protection plan, or message about onlinemobile banking (if the customer has not yet used mobile banking). Thesystem 510 includes a customer interface 512 (e.g., a computing devicesuch as a personal computer, a laptop, a cellular phone, a smart phone,a PDA, etc.) via which a customer logs into a web page 514 of, forexample, a financial institution 516 (e.g., a bank or the like). Thecustomer interface 512 may optionally include a phone 517 by which theuser calls in to the financial institution. In another example, thephone 517 is separate from the customer interface 512. The financialinstitution 514 maintains a customer database 518, which may includecustomer information such as identity, age, bank accounts, transactionhistory, and other related information.

The system 510 further includes a third party server 520, a processor522, and a memory 524. Although the processor 522 and memory 524 areshown as being separate from the server 520, it will be appreciated thatthe processor 522 and memory 524 may be integral thereto. The memory 524stores anonymized customer profiles 526, which include one or more keylifestyle indicators (KLIs) 528 (e.g., customer classifications asdescribed above with regard to the preceding figures) and relatedinformation as described herein. For instance, one KLI for a givencustomer may be “golfer,” where the anonymized customer profile 526includes the “golfer” KLI to indicate that the customer is a golfer, andwherein the “golfer” KLI is assigned to the customer's anonymizedprofile because the customer's transaction data (e.g., stored in thecustomer database 518) indicates that the customer has made or regularlymakes purchases from vendors of golf paraphernalia (e.g., golf clubs,golf magazines, greens fees, country club membership renewals, etc.)from golf equipment retailers, golf clubhouses, etc.

The memory 524 stores current offer information for advertisements thatmay be targeted to a given customer based on the customer's KLIs. Acustomer-offer matching algorithm 532 (e.g., the processing algorithmdescribed with regard to FIG. 12) stored in the memory 524 is executedby the processor 522 to identify one or more offers 530 that may betargeted to the customer.

Another KLI may be “automobile buyer/leasor” based on a transactionhistory that shows regular lease payments to a car dealership. In thiscase, a target advertisement from the financial institution 516 may bean offer for a pre-approved auto loan or lease. For example, if thecustomer's transaction history shows 33 consecutive lease payments, thenit may be assumed that the customer's lease will expire in approximately3 months and the customer may be in the market for an automobile loan.In this case, the matching algorithm 532 identifies an offer for apre-approved auto loan or the like, and the processor 522 sends, via theserver 520, a message to the financial institution 516 to notify thecustomer of the offer.

According to an example, when a user calls into the financialinstitution 516, the user accesses the VRU 534 (e.g., to navigate anautomated menu or the like). The user is prompted to identify himself orherself (e.g., by account number, social security number, or the like).The financial institution identifies the customer in the customerdatabase by a unique customer identification code (UCIC) 536 assigned bythe financial institution. The UCIC does not include the financialinstitution's customer password or unique user ID used to log onto thefinancial institution's website, nor does it include the customer'saccount number, name, street address, social security number, e-mailaddress or telephone number, etc. The UCIC is matched by the server 520and/or processor 522 to an anonymous identifier, described herein as anadvertisement delivery identification code (ADIC) 538, which anonymouslybut uniquely identifies the user. Matching the UDIC 536 to an ADIC 538may be performed by identifying an anonymous customer profile associatedwith the UCIC, and reading an ADIC from the anonymous customer profile,where the ADIC is indicative of KLIs in the profile. The processor thenreads or analyzes a lookup table (LUT) 540 that corresponds ADICs tocurrent offers. Both the ADIC and the UCIC are described in more detailin U.S. patent application Ser. No. 11/865,466, which is herebyincorporated by reference in its entirety herein. In this manner, thematching algorithm 532 identifies current offers that match thecustomer's KLIs 528 and/or ADIC. Both the ADIC and the UCIC aredescribed in more detail in U.S. patent application Ser. No. 11/865,466,which is hereby incorporated by reference in its entirety herein. Oncethe processor retrieves the ADIC for the customer, the customer'sanonymized customer profile 526 is retrieved and the matching algorithm532 identifies current offers that match the customer's KLIs 528. Theidentified offers 530 are then provided to the financial institution 16and a notification thereof is audibly presented to the customer throughthe VRU 534 while the customer is on hold. For instance, the processordelivers a message to the financial institution to instruct the VRU toalert the customer that the targeted offer is available, and that thecustomer should log in to the financial institution's web page 514 toview the offer.

According to another aspect, the notification of the offer is presentedto the customer via the VRU 534, and the customer is asked whether thecustomer would like to receive the offer via a particular medium (e.g.,mail, email, upon login to the financial institution's web page,voicemail, social media, text message, etc.). In order to maintaincustomer anonymity, the email, mail, voicemail, text message, socialmedia message, etc., may be delivered by the financial institution. Ifthe customer selects one or more alternate modes of receiving the offer,the offer is transmitted to the customer using the selected mode. In oneexample, the user is permitted to select an offer (e.g., by pushing abutton or verbally affirming selection, when prompted) and is connectedto the provider of the offer at the end of the call. This allows theuser to finish the business they need to conduct with the bank, andafter which the call is transferred to the end offer provider, whoprovides further offer details.

The processor 522 executes, and the memory 524 stores, computerexecutable instructions for carrying out any and all functions, methods,techniques, etc., described herein. The memory 524 may be acomputer-readable medium on which a control program is stored, such as adisk, hard drive, or the like. Common forms of computer-readable mediainclude, for example, floppy disks, flexible disks, hard disks, magnetictape, or any other magnetic storage medium, CD-ROM, DVD, or any otheroptical medium, RAM, ROM, PROM, EPROM, FLASH-EPROM, variants thereof,other memory chip or cartridge, or any other tangible medium from whichthe processor 516 can read and execute. In this context, the system 510may be implemented on or as one or more general purpose computers,special purpose computer(s), a programmed microprocessor ormicrocontroller and peripheral integrated circuit elements, an ASIC orother integrated circuit, a digital signal processor, a hardwiredelectronic or logic circuit such as a discrete element circuit, aprogrammable logic device such as a PLD, PLA, FPGA, Graphical card CPU(GPU), or PAL, or the like.

FIG. 14 illustrates a system that facilitates presenting targetedcontent directly to a customer when the customer calls in to a business(e.g., a financial institution) and is placed on hold and/or navigates amenu presented by a VRU, wherein the targeted content (e.g., offerdetails) is provided to the VRU 534 for direct, audible presentation tothe customer. As described with regard to FIG. 13, the system 510includes a customer interface 512 (e.g., a computing device such as apersonal computer, a laptop, a cellular phone, a smart phone, a PDA,etc.) via which a customer logs into a web page 514 of, for example, afinancial institution 516 (e.g., a bank or the like). The system furtherincludes a phone 517 by which the user calls in to the financialinstitution. The financial institution 514 maintains a customer database518, which includes customer information such as identity, age, bankaccounts, transaction history, and other related information.

The system 510 further includes a third party server 520, a processor522, and a memory 524. The memory 524 stores anonymized customerprofiles 526, which include one or more key lifestyle indicators (KLIs)528 and related information such as is described herein. The KLIs of agiven customer trigger a specific ADIC to be associated with theanonymized customer profile and it's UCIC. The memory 524 additionallystores current offer information for advertisements that may be targetedto a given customer based on the customer's KLIs. A customer-offermatching algorithm 532 stored in the memory 524 is executed by theprocessor 522 to identify one or more offers 530 that may be targeted tothe customer. The financial institution and/or the server presents theoffer audibly through the voice response unit (VRU) 534.

For example, when a user calls into the financial institution 516, theuser accesses the VRU 534 (e.g., to navigate an automated menu or thelike). The user is prompted to identify himself or herself (e.g., byaccount number, social security number, or the like). The financialinstitution identifies the customer in the customer database by a uniquecustomer identification code (UCIC) 536 assigned by the financialinstitution. The UCIC is matched by the server 520 and/or processor 522to an ADIC 538, which anonymously but uniquely identifies the user.Matching the UDIC 536 to an ADIC 538 may be performed by identifying ananonymous customer profile associated with the UCIC, reading an ADICfrom the anonymous customer profile, where the ADIC is indicative ofKLIs in the profile. The processor then reads or analyzes a lookup table(LUT) 540 that corresponds ADICs to current offers. Both the ADIC andthe UCIC are described in more detail in U.S. patent application Ser.No. 11/865,466, which is hereby incorporated by reference in itsentirety herein. In this manner, the matching algorithm 532 identifiescurrent offers that match the customer's KLIs 528 and/or ADIC. Theidentified offers 530 are then transmitted to the VRU and audiblypresented to the customer through the VRU 534 while the customer is onhold.

According to another aspect, the offer is presented to the user via theVRU 534, and the customer is then asked whether the customer would liketo receive the offer via another medium (e.g., mail, email, upon loginto the financial institution's web page, etc.). If the customer selectsone or more alternate modes of receiving the offer, the offer istransmitted to the customer using the selected mode.

FIG. 15 illustrates a method for notifying a customer, while thecustomer is interacting with a VRU, that targeted content is availablefor the customer. At 600, a call is received from the customer by a VRUat, or associated with, a financial institution or other business. At602, the customer is identified. Identification of the customer may beperformed, for instance, using caller ID, customer-specific information(e.g., social security number, customer ID number, etc.), or some othersuitable identification information. At 604, the customer's anonymizedprofiled is retrieved. Retrieval of the anonymized customer profile maybe performed using a UCIC associated with the customer, which is used toidentify a corresponding ADIC that is associated with the anonymizedcustomer profile. The anonymized customer profile includes KLIsdescribing the customer, and the KLIs are represented by the ADICassigned thereto, which in turn is used to identify offers or othertargeted content relevant to the customer, at 606. At 608, the customeris informed of the offer(s) and/or other content through the VRU.Optionally, at 610, the customer is asked whether the customer desiresto receive details of the offer through one or more different media(e.g., mail, email, text message, presentation of the offer details onthe website of the financial institution upon a next login by thecustomer, etc). If the customer indicates a desired mode of receivingthe offer details, the details are provided to the customer in theselected manner. The method of FIG. 15 may be executed on a computingdevice or system 612, described in greater detail below.

FIG. 16 illustrates a method for notifying a customer, while thecustomer is interacting with a VRU, that targeted content is availablefor the customer. At 620, a call is received from the customer by a VRUat, or associated with, a financial institution or other business. At622, the customer is identified, for instance using caller ID,customer-specific information (e.g., social security number, customer IDnumber, etc.), or some other suitable identification information. At624, the customer's anonymized profiled is retrieved. Retrieval of theanonymized customer profile may be performed using a UCIC associatedwith the customer, which is used to identify a corresponding ADIC thatis associated with the anonymized customer profile. The anonymizedcustomer profile includes KLIs describing the customer, and the KLIs arerepresented by the ADIC assigned thereto, which in turn is used toidentify offers or other targeted content relevant to the customer, at626. At 628, details of the offer are verbally presented to the customerthrough the VRU. Optionally, at 630, the customer is asked whether thecustomer desires also to receive details of the offer through one ormore different media (e.g., mail, email, text message, voicemailmessage, social media message, presentation of the offer details on thewebsite of the financial institution upon a next login by the customer,etc). If the customer indicates a desired mode of receiving the offerdetails, the details are provided to the customer in the selectedmanner.

Additionally, the user's profile can be augmented with informationrelating to the fact that the user called the call center, the reason(s)for the call, and the frequency of the calls. For instance, if a usercalls in and selects options in an automated menu to navigate to acustomer service representative for an auto loan, then the user'sprofile and/or KLIs can be updated to reflect that the user isinterested in purchasing or leasing a vehicle. This feature improves theability of the system to position targeted items and services online orvia a mobile interface.

The methods illustrated in FIGS. 15 and 16 may be implemented in acomputer program product that may be executed on a computer 612 orcomputing components (e.g., server 520, processor 522, memory 524, etc.)in the systems of FIGS. 13 and 14. Further, it is to be appreciated thatany suitable computing environment can be employed in accordance withthe present embodiments. For example, computing architectures including,but not limited to, stand alone, multiprocessor, distributed,client/server, minicomputer, mainframe, supercomputer, digital andanalog can be employed in accordance with the present embodiments.

The computer can include a processing unit such as the processor 522 ofFIGS. 13 and 14, a system memory such as the memory 524 of FIGS. 13 and14, and a system bus that couples various system components includingthe system memory to the processing unit. The processing unit can be anyof various commercially available processors (e.g., a central processingunit, a graphical processing unit, etc.). Dual microprocessors and othermulti-processor architectures also can be used as the processing unit.

The system bus can be any of several types of bus structure including amemory bus or memory controller, a peripheral bus, and a local bus usingany of a variety of commercially available bus architectures. Thecomputer memory includes read only memory (ROM) and random access memory(RAM). A basic input/output system (BIOS), containing the basic routinesthat help to transfer information between elements within the computer,such as during start-up, is stored in ROM.

The computer can further include a hard disk drive, a magnetic diskdrive, e.g., to read from or write to a removable disk, and an opticaldisk drive, e.g., for reading a CD-ROM disk or to read from or write toother optical media. The computer typically includes at least some formof computer readable media. Computer readable media can be any availablemedia that can be accessed by the computer. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other magnetic storage devices, or any other medium which can be usedto store the desired information and which can be accessed by thecomputer.

Communication media typically embodies computer readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of any ofthe above can also be included within the scope of computer readablemedia.

A number of program modules may be stored in the drives and RAM,including an operating system, one or more application programs, otherprogram modules, and program non-interrupt data. The operating system inthe computer can be any of a number of commercially available operatingsystems.

A user may enter commands and information into the computer through akeyboard (not shown) and a pointing device or stylus (not shown), suchas a mouse. Other input devices (not shown) may include a microphone, anIR remote control, a joystick, a game pad, a satellite dish, a scanner,or the like. These and other input devices are often connected to theprocessing unit through a serial port interface (not shown) that iscoupled to the system bus, but may be connected by other interfaces,such as a parallel port, a game port, a universal serial bus (USB), anIR interface, etc.

A monitor (not shown), or other type of display device, may also beconnected to the system bus via an interface, such as a video adapter(not shown). In addition to the monitor, a computer typically includesother peripheral output devices (not shown), such as speakers, printersetc. The monitor can be employed with the computer to present data thatis electronically received from one or more disparate sources. Forexample, the monitor can be an LCD, plasma, CRT, etc. type that presentsdata electronically. Alternatively or in addition, the monitor candisplay received data in a hard copy format such as a printer,facsimile, plotter etc. The monitor can present data in any color andcan receive data from the computer via any wireless or hard wireprotocol and/or standard.

The computer can operate in a networked environment using logical and/orphysical connections to one or more remote computers, such as a remotecomputer(s). The remote computer(s) can be a workstation, a servercomputer, a router, a personal computer, microprocessor basedentertainment appliance, a peer device or other common network node, andtypically includes many or all of the elements described relative to thecomputer. The logical connections depicted include a local area network(LAN) and a wide area network (WAN). Such networking environments arecommonplace in offices, enterprise-wide computer networks, intranets andthe Internet.

When used in a LAN networking environment, the computer is connected tothe local network through a network interface or adapter. When used in aWAN networking environment, the computer typically includes a modem, oris connected to a communications server on the LAN, or has other meansfor establishing communications over the WAN, such as the Internet. In anetworked environment, program modules depicted relative to thecomputer, or portions thereof, may be stored in the remote memorystorage device. It will be appreciated that network connectionsdescribed herein are exemplary and other means of establishing acommunications link between the computers may be used.

Methods and systems for providing targeted content to an on-holdcustomer based on classifications have been described with reference tospecific embodiments. Modifications and alterations will occur to thoseupon reading and understanding the preceding detailed description. Forexample, the methods and systems described above can also be used todeliver targeted content—content that is not an advertisement—toconsumers based on the above described classification and transactionalmetrics. The invention is not limited to only those embodimentsdescribed above. Instead, the invention is intended to cover allmodifications and alterations that come within the scope of the appendedclaims and the equivalents thereof.

1. A method for presenting targeted content to a customer of a financialinstitution through a voice response unit (VRU), the method comprising:receiving a call from a customer at a VRU; identifying the customer;retrieving an anonymized customer profile for the customer; matching oneor more targeted offers to the customer as a function of key lifestyleindicators (KLIs) in the anonymized customer profile; and verballyinforming the customer through the VRU that the one or more targetedoffers is available to the customer.
 2. The method of claim 1, furthercomprising presenting offer details verbally to the customer via theVRU.
 3. The method of claim 2, further comprising: receiving customerinput relate to at least one other selected mode of delivery of theoffer details.
 4. The method of claim 3, wherein the at least one otherselected mode of delivery is one or more of mail, email, text messageand presentation on a webpage of the financial institution upon thecustomer logging on to the web page.
 5. The method of claim 1, furthercomprising: receiving customer input related to at least one selectedmode of delivery of details of the one or more targeted offers.
 6. Themethod of claim 5, wherein the at least one selected mode of delivery isone or more of mail, email, text message and presentation on a webpageof the financial institution upon the customer logging on to the webpage.
 7. The method of claim 1, wherein retrieving the anonymizedcustomer profile comprises: identifying a unique customer identificationcode (UCIC) associated with the customer and assigned by the financialinstitution; and identifying the anonymized customer profile associatedwith the identified UCIC.
 8. The method of claim 7, wherein identifyingthe one or more targeted offers comprises: identifying an advertisementidentification code (ADIC) associated with the anonymized customerprofile and representing the KLIs therein; and identifying one or moretargeted offers associated with the ADIC.
 9. The method of claim 1,wherein the KLIs describe customer attributes that are determined fromthe customer's financial transaction history.
 10. A system forpresenting targeted content to a customer of a financial institutionthrough a voice response unit (VRU), the system comprising: a voiceresponse system (VRU) that receives a call from the customer, andreceives customer identification information by which the customer isidentified; a third-party server comprising a processor and a memory;wherein the processor executes computer-executable instructions for:retrieving an anonymized customer profile for the customer; andidentifying one or more targeted offers for the customer as a functionof key lifestyle indicators (KLIs) in the anonymized customer profile;wherein the VRU informs the customer that the one or more identifiedtargeted offers is available to the customer.
 11. The system of claim10, the instructions further comprising presenting offer detailsverbally to the customer via the VRU.
 12. The system of claim 11, theinstructions further comprising: receiving customer input relate to atleast one other selected mode of delivery of the offer details.
 13. Thesystem of claim 12, wherein the at least one other selected mode ofdelivery is one or more of mail, email, text message and presentation ona webpage of the financial institution upon the customer logging on tothe web page.
 14. The system of claim 10, the instructions furthercomprising: receiving customer input related to at least one selectedmode of delivery of details of the one or more targeted offers.
 15. Thesystem of claim 14, wherein the at least one selected mode of deliveryis one or more of mail, email, text message and presentation on awebpage of the financial institution upon the customer logging on to theweb page
 16. The system of claim 10, wherein the instructions forretrieving the anonymized customer profile comprise instructions for:identifying a unique customer identification code (UCIC) associated withthe customer and assigned by the financial institution; and identifyingthe anonymized customer profile associated with the identified UCIC. 17.The system of claim 16, wherein the instructions for identifying the oneor more targeted offers include instructions for: identifying anadvertisement identification code (ADIC) associated with the anonymizedcustomer profile and representing the KLIs therein; and identifying oneor more targeted offers associated with the ADIC.
 18. The system ofclaim 10, wherein the KLIs describe customer attributes that aredetermined from the customer's financial transaction history stored atthe financial institution.